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  1. Article ; Online: Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

    McNamara, Mary E / Zisser, Mackenzie / Beevers, Christopher G / Shumake, Jason

    Behaviour research and therapy

    2022  Volume 153, Page(s) 104086

    Abstract: There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular ... ...

    Abstract There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular machine learning models to the prediction of treatment prognosis, with a particular focus on digital interventions. Assuming that the prediction of treatment prognosis will involve modeling a complex combination of interacting features with measurement error in both the predictors and outcomes, our simulations suggest that to optimize complex prediction models, sample sizes in the thousands will be required. Machine learning methods capable of discovering complex interactions and nonlinear effects (e.g., decision tree ensembles such as gradient boosted machines) perform particularly well in large samples when the predictors and outcomes have virtually no measurement error. However, in the presence of moderate measurement error, these methods provide little or no benefit over regularized linear regression, even with very large sample sizes (N = 100,000) and a non-linear ground truth. Given these sample size requirements, we argue that the scalability of digital interventions, especially when used in combination with optimal measurement practices, provides one of the most effective ways to study treatment prediction models. We conclude with suggestions about how to implement these algorithms into clinical practice.
    MeSH term(s) Algorithms ; Humans ; Linear Models ; Machine Learning ; Prognosis ; Sample Size
    Language English
    Publishing date 2022-04-14
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 211997-3
    ISSN 1873-622X ; 0005-7967
    ISSN (online) 1873-622X
    ISSN 0005-7967
    DOI 10.1016/j.brat.2022.104086
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  2. Article ; Online: The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding.

    Pisner, D A / Shumake, J / Beevers, C G / Schnyer, D M

    NeuroImage. Clinical

    2019  Volume 24, Page(s) 101935

    Abstract: Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual ... ...

    Abstract Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R
    MeSH term(s) Adolescent ; Adult ; Attention/physiology ; Brain/diagnostic imaging ; Brain/physiology ; Depressive Disorder, Major/diagnostic imaging ; Depressive Disorder, Major/physiopathology ; Depressive Disorder, Major/psychology ; Executive Function/physiology ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Nerve Net/diagnostic imaging ; Nerve Net/physiology ; White Matter/diagnostic imaging ; White Matter/physiology ; Young Adult
    Language English
    Publishing date 2019-07-19
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2701571-3
    ISSN 2213-1582 ; 2213-1582
    ISSN (online) 2213-1582
    ISSN 2213-1582
    DOI 10.1016/j.nicl.2019.101935
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  3. Article: Examining the long-term effects of traumatic brain injury on fear extinction in male rats.

    Smith, K A / Raskin, M R / Donovan, M H / Raghunath, V / Mansoorshahi, S / Telch, M J / Shumake, J / Noble-Haeusslein, L J / Monfils, M H

    Frontiers in behavioral neuroscience

    2023  Volume 17, Page(s) 1206073

    Abstract: There is a strong association between traumatic brain injuries (TBIs) and the development of psychiatric disorders, including post-traumatic stress disorder (PTSD). Exposure-based therapy is a first-line intervention for individuals who suffer from PTSD ... ...

    Abstract There is a strong association between traumatic brain injuries (TBIs) and the development of psychiatric disorders, including post-traumatic stress disorder (PTSD). Exposure-based therapy is a first-line intervention for individuals who suffer from PTSD and other anxiety-related disorders; however, up to 50% of individuals with PTSD do not respond well to this approach. Fear extinction, a core mechanism underlying exposure-based therapy, is a procedure in which a repeated presentation of a conditioned stimulus in the absence of an unconditioned stimulus leads to a decrease in fear expression, and is a useful tool to better understand exposure-based therapy. Identifying predictors of extinction would be useful in developing alternative treatments for the non-responders. We recently found that CO
    Language English
    Publishing date 2023-06-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452960-6
    ISSN 1662-5153
    ISSN 1662-5153
    DOI 10.3389/fnbeh.2023.1206073
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  4. Article: Web-Based Single Session Intervention for Perceived Control Over Anxiety During COVID-19: Randomized Controlled Trial.

    Mullarkey, Michael / Dobias, Mallory / Sung, Jenna / Ahuvia, Isaac / Shumake, Jason / Beevers, Christopher / Schleider, Jessica

    JMIR mental health

    2022  Volume 9, Issue 4, Page(s) e33473

    Abstract: Background: Anxiety is rising across the United States during the COVID-19 pandemic, and social distancing mandates preclude in-person mental health care. Greater perceived control over anxiety has predicted decreased anxiety pathology, including ... ...

    Abstract Background: Anxiety is rising across the United States during the COVID-19 pandemic, and social distancing mandates preclude in-person mental health care. Greater perceived control over anxiety has predicted decreased anxiety pathology, including adaptive responses to uncontrollable stressors. Evidence suggests that no-therapist, single-session interventions can strengthen perceived control over emotions like anxiety; similar programs, if designed for the COVID-19 context, could hold substantial public health value.
    Objective: Our registered report evaluated a no-therapist, single-session, online intervention targeting perceived control over anxiety in the COVID-19 context against a placebo intervention encouraging handwashing. We tested whether the intervention could (1) decrease generalized anxiety and increase perceived control over anxiety and (2) achieve this without decreasing social-distancing intentions.
    Methods: We tested these questions using a between-subjects design in a weighted-probability sample of US adults recruited via a closed online platform (ie, Prolific). All outcomes were indexed via online self-report questionnaires.
    Results: Of 522 randomized individuals, 500 (95.8%) completed the baseline survey and intervention. Intent-to-treat analyses using all randomized participants (N=522) found no support for therapeutic or iatrogenic effects; effects on generalized anxiety were d=-0.06 (95% CI -0.27 to 0.15; P=.48), effects on perceived control were d=0.04 (95% CI -0.08 to 0.16; P=.48), and effects on social-distancing intentions were d=-0.02 (95% CI -0.23 to 0.19; P=.83).
    Conclusions: Strengths of this study included a large, nationally representative sample and adherence to open science practices. Implications for scalable interventions, including the challenge of targeting perceived control over anxiety, are discussed.
    Trial registration: ClinicalTrials.gov NCT04459455; https://clinicaltrials.gov/show/NCT04459455.
    Language English
    Publishing date 2022-04-12
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2798262-2
    ISSN 2368-7959
    ISSN 2368-7959
    DOI 10.2196/33473
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  5. Article ; Online: An examination of the clinical utility of phonemic fluency in healthy adults and adults with mild cognitive impairment.

    Dekhtyar, Maria / Foret, Janelle T / Simon, Sarah / Shumake, Jason / Clark, Alexandra L / Haley, Andreana P

    Applied neuropsychology. Adult

    2022  , Page(s) 1–9

    Abstract: The Controlled Oral Word Association Test (COWAT) is a widely utilized measure of phonemic fluency. However, two issues remain: (1) whether demographic, cognitive variables, or version of test administered predict performance; (2) if the test is ... ...

    Abstract The Controlled Oral Word Association Test (COWAT) is a widely utilized measure of phonemic fluency. However, two issues remain: (1) whether demographic, cognitive variables, or version of test administered predict performance; (2) if the test is predictive of Mild Cognitive Impairment (MCI). Recent studies report that item-level analyses such as lexical frequency may be more sensitive to early cognitive change. The purpose of this study was to examine the clinical utility of the COWAT, considering both total correct words and the lexical frequency. Sixty-seven healthy adults and thirty-seven adults with MCI completed neuropsychological testing. Mann-Whitney
    Language English
    Publishing date 2022-04-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2673736-X
    ISSN 2327-9109 ; 2327-9095
    ISSN (online) 2327-9109
    ISSN 2327-9095
    DOI 10.1080/23279095.2022.2061860
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4952: Show issues Location:
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  6. Article ; Online: Altered electroencephalography resting state network coherence in remitted MDD.

    Ray, Kimberly L / Griffin, Nicholas R / Shumake, Jason / Alario, Alexandra / Allen, John J B / Beevers, Christopher G / Schnyer, David M

    Brain research

    2023  Volume 1806, Page(s) 148282

    Abstract: Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting- ... ...

    Abstract Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.
    MeSH term(s) Adult ; Humans ; Neural Pathways/physiology ; Depressive Disorder, Major ; Electroencephalography ; Brain/physiology ; Brain Mapping ; Magnetic Resonance Imaging
    Language English
    Publishing date 2023-02-13
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1200-2
    ISSN 1872-6240 ; 0006-8993
    ISSN (online) 1872-6240
    ISSN 0006-8993
    DOI 10.1016/j.brainres.2023.148282
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  7. Article ; Online: Carbon Dioxide Reactivity Differentially Predicts Fear Expression After Extinction and Retrieval-Extinction in Rats.

    Raskin, Marissa / Keller, Nicole E / Agee, Laura A / Shumake, Jason / Smits, Jasper A J / Telch, Michael J / Otto, Michael W / Lee, Hongjoo J / Monfils, Marie-H

    Biological psychiatry global open science

    2024  Volume 4, Issue 3, Page(s) 100310

    Abstract: Background: Cues present during a traumatic event may result in persistent fear responses. These responses can be attenuated through extinction learning, a core component of exposure therapy. Exposure/extinction is effective for some people, but not all. ...

    Abstract Background: Cues present during a traumatic event may result in persistent fear responses. These responses can be attenuated through extinction learning, a core component of exposure therapy. Exposure/extinction is effective for some people, but not all. We recently demonstrated that carbon dioxide (CO
    Methods: Male rats first underwent a CO
    Results: We found that retrieval-extinction resulted in lower freezing during extinction, LTM, and reinstatement than standard extinction. Using the best subset approach to linear regression, we found that CO
    Conclusions: CO
    Language English
    Publishing date 2024-03-22
    Publishing country United States
    Document type Journal Article
    ISSN 2667-1743
    ISSN (online) 2667-1743
    DOI 10.1016/j.bpsgos.2024.100310
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  8. Article ; Online: Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users.

    Weisenburger, Rachel L / Mullarkey, Michael C / Labrada, Jocelyn / Labrousse, Daniel / Yang, Michelle Y / MacPherson, Allison Huff / Hsu, Kean J / Ugail, Hassan / Shumake, Jason / Beevers, Christopher G

    Journal of affective disorders

    2024  Volume 351, Page(s) 489–498

    Abstract: Background: Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient ... ...

    Abstract Background: Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient alternative to other screening methods.
    Objective: The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity.
    Methods: Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred.
    Results: There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90 % of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity.
    Limitations: Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology.
    Conclusion: The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool on par with self-report measures.
    MeSH term(s) Adult ; Humans ; Artificial Intelligence ; Depression/diagnosis ; Communication ; Ethnicity ; Internet
    Language English
    Publishing date 2024-01-28
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 135449-8
    ISSN 1573-2517 ; 0165-0327
    ISSN (online) 1573-2517
    ISSN 0165-0327
    DOI 10.1016/j.jad.2024.01.212
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    Zs.A 1485: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response.

    Shumake, Jason / Mallard, Travis T / McGeary, John E / Beevers, Christopher G

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 3780

    Abstract: Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant ... ...

    Abstract Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.
    MeSH term(s) Antidepressive Agents/metabolism ; Antidepressive Agents/therapeutic use ; Area Under Curve ; Biomarkers, Pharmacological/analysis ; Citalopram/pharmacology ; Databases, Factual ; Databases, Genetic ; Decision Trees ; Depression/drug therapy ; Depression/genetics ; Depressive Disorder, Major/drug therapy ; Depressive Disorder, Major/genetics ; Drug-Related Side Effects and Adverse Reactions ; Genetic Variation/genetics ; Humans ; Logistic Models ; Machine Learning ; Polymorphism, Single Nucleotide/drug effects ; Polymorphism, Single Nucleotide/genetics ; Prognosis ; Treatment Outcome
    Chemical Substances Antidepressive Agents ; Biomarkers, Pharmacological ; Citalopram (0DHU5B8D6V)
    Language English
    Publishing date 2021-02-12
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-83338-2
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  10. Article ; Online: Change in negative attention bias mediates the association between attention bias modification training and depression symptom improvement.

    Beevers, Christopher G / Hsu, Kean J / Schnyer, David M / Smits, Jasper A J / Shumake, Jason

    Journal of consulting and clinical psychology

    2021  Volume 89, Issue 10, Page(s) 816–829

    Abstract: Objective: Attention bias modification training (ABMT) is purported to reduce depression by targeting and modifying an attentional bias for sadness-related stimuli. However, few tests of this hypothesis have been completed.: Method: The present study ...

    Abstract Objective: Attention bias modification training (ABMT) is purported to reduce depression by targeting and modifying an attentional bias for sadness-related stimuli. However, few tests of this hypothesis have been completed.
    Method: The present study examined whether change in attentional bias mediated a previously reported association between ABMT condition (active ABMT, sham ABMT, assessments only; N = 145) and depression symptom change among depressed adults. The preregistered, primary measure of attention bias was a discretized eye-tracking metric that quantified the proportion of trials where gaze time was greater for sad stimuli than neutral stimuli.
    Results: Contemporaneous longitudinal simplex mediation indicated that change in attentional bias early in treatment partially mediated the effect of ABMT on depression symptoms. Specificity analyses indicated that in contrast to the eye-tracking mediator, reaction time assessments of attentional bias for sad stimuli (mean bias and trial level variability) and lapses in sustained attention did not mediate the association between ABMT and depression change. Results also suggested that mediation effects were limited to a degree by suboptimal measurement of attentional bias for sad stimuli.
    Conclusion: When effective, ABMT may improve depression in part by reducing an attentional bias for sad stimuli, particularly early on during ABMT. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
    MeSH term(s) Adult ; Attentional Bias ; Cognition ; Depression/therapy ; Eye-Tracking Technology ; Humans ; Mental Disorders
    Language English
    Publishing date 2021-12-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 121321-0
    ISSN 1939-2117 ; 0022-006X
    ISSN (online) 1939-2117
    ISSN 0022-006X
    DOI 10.1037/ccp0000683
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